Overview

Dataset statistics

Number of variables16
Number of observations200
Missing cells30
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.1 KiB
Average record size in memory128.6 B

Variable types

NUM15
DATE1

Warnings

turnover_vol_5d_min is highly correlated with VSTD_6d_minHigh correlation
VSTD_6d_min is highly correlated with turnover_vol_5d_minHigh correlation
NCFP_min is highly correlated with n_cashflow_actHigh correlation
n_cashflow_act is highly correlated with NCFP_minHigh correlation
turnover_vol_20d_max has 2 (1.0%) missing values Missing
VSTD_6d_min has 2 (1.0%) missing values Missing
current_ratio has 2 (1.0%) missing values Missing
TO_20d_min has 2 (1.0%) missing values Missing
turnover_vol_100d_min has 2 (1.0%) missing values Missing
assets_turn has 2 (1.0%) missing values Missing
VSTD_6d_max has 2 (1.0%) missing values Missing
PPReversal_20_min has 2 (1.0%) missing values Missing
n_cashflow_act has 2 (1.0%) missing values Missing
turnover_vol_5d_min has 2 (1.0%) missing values Missing
DAVOL5_min has 2 (1.0%) missing values Missing
opincome_of_ebt has 2 (1.0%) missing values Missing
NCFP_min has 2 (1.0%) missing values Missing
TO_100d_min has 2 (1.0%) missing values Missing
BP_LF_min has 2 (1.0%) missing values Missing
date has unique values Unique

Reproduction

Analysis started2020-12-18 14:46:25.968574
Analysis finished2020-12-18 14:46:59.560434
Duration33.59 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

date
Date

UNIQUE

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2013-10-06 00:00:00
Maximum2017-07-30 00:00:00
2020-12-18T22:46:59.638854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:59.818696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

turnover_vol_20d_max
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.01803920759
Minimum-0.8594448417
Maximum1.852643462
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:46:59.969355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8594448417
5-th percentile-0.8064563011
Q1-0.582507573
median-0.2450360153
Q30.3940311046
95-th percentile1.579765267
Maximum1.852643462
Range2.712088303
Interquartile range (IQR)0.9765386776

Descriptive statistics

Standard deviation0.718708139
Coefficient of variation (CV)-39.84144732
Kurtosis0.2029796373
Mean-0.01803920759
Median Absolute Deviation (MAD)0.3931783933
Skewness1.060608737
Sum-3.571763104
Variance0.5165413891
MonotocityNot monotonic
2020-12-18T22:47:00.145815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.611890267521.0%
 
0.63774566821.0%
 
1.16967110910.5%
 
1.70985626310.5%
 
-0.177844497710.5%
 
-0.114277390710.5%
 
-0.33367391110.5%
 
1.52772514110.5%
 
-0.743215858810.5%
 
-0.00661746976210.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.859444841710.5%
 
-0.857890636410.5%
 
-0.853669164410.5%
 
-0.847036880210.5%
 
-0.836320715210.5%
 
ValueCountFrequency (%) 
1.85264346210.5%
 
1.83306953710.5%
 
1.74664483410.5%
 
1.73066832810.5%
 
1.70985626310.5%
 

VSTD_6d_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct185
Distinct (%)93.4%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2929415445
Minimum-0.8088934028
Maximum2.987844599
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:00.306546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8088934028
5-th percentile-0.7203368437
Q1-0.53117216
median-0.1316028595
Q30.7376263328
95-th percentile2.987844599
Maximum2.987844599
Range3.796738001
Interquartile range (IQR)1.268798493

Descriptive statistics

Standard deviation1.122080207
Coefficient of variation (CV)3.830389468
Kurtosis0.4921920124
Mean0.2929415445
Median Absolute Deviation (MAD)0.4736604724
Skewness1.282401738
Sum58.00242581
Variance1.259063991
MonotocityNot monotonic
2020-12-18T22:47:00.470520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.987844599147.0%
 
0.363211036210.5%
 
0.196161343510.5%
 
0.303821228410.5%
 
0.146551704510.5%
 
-0.762005517310.5%
 
1.57810626210.5%
 
0.716892104710.5%
 
-0.662417670410.5%
 
-0.651180301910.5%
 
Other values (175)17587.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.808893402810.5%
 
-0.790437478810.5%
 
-0.783987585710.5%
 
-0.764025312810.5%
 
-0.762037656510.5%
 
ValueCountFrequency (%) 
2.987844599147.0%
 
2.90816147210.5%
 
2.77411162610.5%
 
2.67490440610.5%
 
2.45553108910.5%
 

current_ratio
Real number (ℝ)

MISSING

Distinct25
Distinct (%)12.6%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.5642259945
Minimum-0.7565471019
Maximum-0.3586473603
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:00.620155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7565471019
5-th percentile-0.7032478063
Q1-0.6612233617
median-0.5419149384
Q3-0.4945605642
95-th percentile-0.3586473603
Maximum-0.3586473603
Range0.3978997416
Interquartile range (IQR)0.1666627975

Descriptive statistics

Standard deviation0.09844040508
Coefficient of variation (CV)-0.1744698153
Kurtosis-0.6818555016
Mean-0.5642259945
Median Absolute Deviation (MAD)0.06539413579
Skewness0.07981543875
Sum-111.7167469
Variance0.009690513353
MonotocityNot monotonic
2020-12-18T22:47:00.762151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
-0.7032478063136.5%
 
-0.6636833292136.5%
 
-0.5144453014136.5%
 
-0.5132153176136.5%
 
-0.6925879472136.5%
 
-0.6612233617136.5%
 
-0.5525747975126.0%
 
-0.5419149384126.0%
 
-0.5257201524126.0%
 
-0.3586473603126.0%
 
Other values (15)7236.0%
 
ValueCountFrequency (%) 
-0.756547101942.0%
 
-0.7032478063136.5%
 
-0.6925879472136.5%
 
-0.6636833292136.5%
 
-0.6612233617136.5%
 
ValueCountFrequency (%) 
-0.3586473603126.0%
 
-0.38054107110.5%
 
-0.441261268610.5%
 
-0.468115913710.5%
 
-0.4681159137126.0%
 

TO_20d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2836789397
Minimum-0.8630996896
Maximum2.603854558
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:00.965516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8630996896
5-th percentile-0.800821967
Q1-0.6130921575
median-0.02417623686
Q30.8719919972
95-th percentile2.193930371
Maximum2.603854558
Range3.466954248
Interquartile range (IQR)1.485084155

Descriptive statistics

Standard deviation0.9820311789
Coefficient of variation (CV)3.461769774
Kurtosis-0.6213726183
Mean0.2836789397
Median Absolute Deviation (MAD)0.6494842912
Skewness0.7346964737
Sum56.16843006
Variance0.9643852363
MonotocityNot monotonic
2020-12-18T22:47:01.134847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.3787690621.0%
 
1.93185107621.0%
 
0.502114835610.5%
 
-0.53008277310.5%
 
0.364346032710.5%
 
1.28941353310.5%
 
1.56084469810.5%
 
0.874124479810.5%
 
1.29973283810.5%
 
-0.0800811588510.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.863099689610.5%
 
-0.859012793110.5%
 
-0.842643491510.5%
 
-0.830048380110.5%
 
-0.823253534610.5%
 
ValueCountFrequency (%) 
2.60385455810.5%
 
2.53748049310.5%
 
2.44851557510.5%
 
2.43979237410.5%
 
2.3787690621.0%
 

turnover_vol_100d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.130299846
Minimum-0.8433017694
Maximum0.7948672307
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:01.300502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8433017694
5-th percentile-0.8219696378
Q1-0.6930164015
median-0.2170703782
Q30.4727252469
95-th percentile0.6641441608
Maximum0.7948672307
Range1.638169
Interquartile range (IQR)1.165741648

Descriptive statistics

Standard deviation0.5533446086
Coefficient of variation (CV)-4.246701939
Kurtosis-1.499819705
Mean-0.130299846
Median Absolute Deviation (MAD)0.5360287943
Skewness0.2046058442
Sum-25.79936951
Variance0.3061902559
MonotocityNot monotonic
2020-12-18T22:47:01.449285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.277699059621.0%
 
0.435948502521.0%
 
-0.44876289510.5%
 
-0.376749285610.5%
 
-0.0267691095610.5%
 
0.564452440210.5%
 
0.197072865210.5%
 
0.0143533573810.5%
 
-0.787853670410.5%
 
-0.0895424633410.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.843301769410.5%
 
-0.834881113510.5%
 
-0.833600696110.5%
 
-0.83348859110.5%
 
-0.833255266710.5%
 
ValueCountFrequency (%) 
0.794867230710.5%
 
0.774421173110.5%
 
0.772327153410.5%
 
0.736252835310.5%
 
0.732143100710.5%
 

assets_turn
Real number (ℝ)

MISSING

Distinct26
Distinct (%)13.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.4961992795
Minimum-1.054097199
Maximum0.2428309472
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:01.588403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.054097199
5-th percentile-1.054097199
Q1-0.8409570894
median-0.684448982
Q3-0.04519978609
95-th percentile0.2428309472
Maximum0.2428309472
Range1.296928146
Interquartile range (IQR)0.7957573033

Descriptive statistics

Standard deviation0.4353962059
Coefficient of variation (CV)-0.8774623905
Kurtosis-1.246147642
Mean-0.4961992795
Median Absolute Deviation (MAD)0.3676736006
Skewness0.2431794362
Sum-98.24745733
Variance0.1895698561
MonotocityNot monotonic
2020-12-18T22:47:01.730163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-1.052122583136.5%
 
-0.6969548868136.5%
 
-0.3040061945136.5%
 
0.1238273901136.5%
 
-0.007813712853136.5%
 
-1.038300267126.0%
 
-0.7542187666126.0%
 
-0.684448982126.0%
 
-0.6949802703126.0%
 
-0.2858397223126.0%
 
Other values (16)7336.5%
 
ValueCountFrequency (%) 
-1.054097199115.5%
 
-1.052122583136.5%
 
-1.038300267126.0%
 
-1.021450206126.0%
 
-0.99248916310.5%
 
ValueCountFrequency (%) 
0.2428309472126.0%
 
0.1238273901136.5%
 
0.1093468688126.0%
 
-0.007813712853136.5%
 
-0.157358005810.5%
 

VSTD_6d_max
Real number (ℝ)

MISSING

Distinct181
Distinct (%)91.4%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2926706999
Minimum-0.7979503122
Maximum2.999371288
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:01.878505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7979503122
5-th percentile-0.7485743975
Q1-0.5263065983
median-0.100972989
Q30.6063935776
95-th percentile2.999371288
Maximum2.999371288
Range3.7973216
Interquartile range (IQR)1.132700176

Descriptive statistics

Standard deviation1.125205429
Coefficient of variation (CV)3.844612492
Kurtosis0.5880756378
Mean0.2926706999
Median Absolute Deviation (MAD)0.4836810884
Skewness1.308332873
Sum57.94879858
Variance1.266087257
MonotocityNot monotonic
2020-12-18T22:47:02.042061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.999371288178.5%
 
2.15218669221.0%
 
-0.374159160710.5%
 
0.0579469690710.5%
 
-0.0347890173810.5%
 
-0.68796226810.5%
 
-0.705529534610.5%
 
0.183392978610.5%
 
1.48760205110.5%
 
-0.115430033410.5%
 
Other values (171)17185.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.797950312210.5%
 
-0.79794533510.5%
 
-0.793507414110.5%
 
-0.774033353710.5%
 
-0.764005603710.5%
 
ValueCountFrequency (%) 
2.999371288178.5%
 
2.45898059610.5%
 
2.36696935910.5%
 
2.15778412610.5%
 
2.15218669221.0%
 

PPReversal_20_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.1180671681
Minimum-2.848572044
Maximum2.691846373
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:02.213866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.848572044
5-th percentile-1.901782382
Q1-0.4480791811
median0.1816867671
Q30.6720140372
95-th percentile1.899906337
Maximum2.691846373
Range5.540418417
Interquartile range (IQR)1.120093218

Descriptive statistics

Standard deviation1.058291991
Coefficient of variation (CV)8.963474
Kurtosis0.4718476234
Mean0.1180671681
Median Absolute Deviation (MAD)0.5575586721
Skewness-0.3422347995
Sum23.37729928
Variance1.119981939
MonotocityNot monotonic
2020-12-18T22:47:02.376448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.10935885121.0%
 
1.81248634521.0%
 
-0.360808844410.5%
 
1.05808573110.5%
 
0.474459521210.5%
 
-0.706027533510.5%
 
-0.64124109110.5%
 
0.296055336910.5%
 
1.06231268410.5%
 
0.562296617510.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-2.84857204410.5%
 
-2.75121007210.5%
 
-2.58111528510.5%
 
-2.51223177910.5%
 
-2.46959677810.5%
 
ValueCountFrequency (%) 
2.69184637310.5%
 
2.49927844910.5%
 
2.29146708110.5%
 
2.26922968810.5%
 
2.24169480510.5%
 

n_cashflow_act
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct26
Distinct (%)13.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.0582831862
Minimum-1.656739778
Maximum1.255993047
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:02.512928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.656739778
5-th percentile-1.656739778
Q1-0.643507077
median-0.08713222998
Q30.556155474
95-th percentile1.255993047
Maximum1.255993047
Range2.912732825
Interquartile range (IQR)1.199662551

Descriptive statistics

Standard deviation0.7639137302
Coefficient of variation (CV)-13.10693152
Kurtosis-0.4819269641
Mean-0.0582831862
Median Absolute Deviation (MAD)0.556374847
Skewness-0.0430312213
Sum-11.54007087
Variance0.5835641872
MonotocityNot monotonic
2020-12-18T22:47:02.651779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-0.9739910697136.5%
 
0.556155474136.5%
 
1.255993047136.5%
 
0.1876779571136.5%
 
-0.643507077136.5%
 
0.0550708916126.0%
 
0.04434183859126.0%
 
-0.7817862558126.0%
 
-0.08713222998126.0%
 
0.7494322841126.0%
 
Other values (16)7336.5%
 
ValueCountFrequency (%) 
-1.656739778126.0%
 
-0.9739910697136.5%
 
-0.7817862558126.0%
 
-0.702390939110.5%
 
-0.643507077136.5%
 
ValueCountFrequency (%) 
1.255993047136.5%
 
1.098600671126.0%
 
1.04181118742.0%
 
0.858919409410.5%
 
0.7494322841126.0%
 

turnover_vol_5d_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct194
Distinct (%)98.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.118795039
Minimum-0.8944611524
Maximum3.614485608
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:02.807221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8944611524
5-th percentile-0.7902017633
Q1-0.5763043958
median-0.1658641626
Q30.4046977117
95-th percentile2.223313856
Maximum3.614485608
Range4.50894676
Interquartile range (IQR)0.9810021076

Descriptive statistics

Standard deviation0.9851021978
Coefficient of variation (CV)8.292452329
Kurtosis3.309890964
Mean0.118795039
Median Absolute Deviation (MAD)0.4562191389
Skewness1.806551218
Sum23.52141772
Variance0.9704263401
MonotocityNot monotonic
2020-12-18T22:47:02.956758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.61448560852.5%
 
0.113428201610.5%
 
-0.521212726410.5%
 
-0.630917345610.5%
 
-0.0729945370310.5%
 
-0.816445993510.5%
 
-0.834016966910.5%
 
1.52786636310.5%
 
0.1651478310.5%
 
2.5192098210.5%
 
Other values (184)18492.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.894461152410.5%
 
-0.889069345810.5%
 
-0.85266551510.5%
 
-0.834016966910.5%
 
-0.824624849110.5%
 
ValueCountFrequency (%) 
3.61448560852.5%
 
3.35500785810.5%
 
2.96757161610.5%
 
2.58242765710.5%
 
2.5192098210.5%
 

DAVOL5_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.08401223169
Minimum-1.001059333
Maximum2.622952506
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:03.117643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.001059333
5-th percentile-0.9088418695
Q1-0.5903304755
median-0.2212133881
Q30.3492463601
95-th percentile1.236884384
Maximum2.622952506
Range3.624011838
Interquartile range (IQR)0.9395768356

Descriptive statistics

Standard deviation0.6721462759
Coefficient of variation (CV)-8.000576373
Kurtosis1.239036014
Mean-0.08401223169
Median Absolute Deviation (MAD)0.4705798996
Skewness1.063742117
Sum-16.63442187
Variance0.4517806162
MonotocityNot monotonic
2020-12-18T22:47:03.286011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.501481050321.0%
 
-0.297544649321.0%
 
-0.700837327610.5%
 
1.57307329110.5%
 
1.09095922610.5%
 
-0.0706784823610.5%
 
2.62295250610.5%
 
-0.714349242610.5%
 
-0.881459828910.5%
 
-0.723546457310.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.00105933310.5%
 
-0.987188519510.5%
 
-0.982656902410.5%
 
-0.976243087610.5%
 
-0.971230058310.5%
 
ValueCountFrequency (%) 
2.62295250610.5%
 
2.07163763910.5%
 
1.9318235510.5%
 
1.57746769710.5%
 
1.57307329110.5%
 

opincome_of_ebt
Real number (ℝ)

MISSING

Distinct22
Distinct (%)11.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.4825840176
Minimum-0.7695906706
Maximum3.982696365
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:03.439061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7695906706
5-th percentile-0.7695906706
Q1-0.4883745934
median-0.08510969522
Q30.6904777086
95-th percentile3.982696365
Maximum3.982696365
Range4.752287036
Interquartile range (IQR)1.178852302

Descriptive statistics

Standard deviation1.489356881
Coefficient of variation (CV)3.08621261
Kurtosis1.385626993
Mean0.4825840176
Median Absolute Deviation (MAD)0.5922053026
Skewness1.632497492
Sum95.55163548
Variance2.218183918
MonotocityNot monotonic
2020-12-18T22:47:03.571919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
3.9826963652713.5%
 
-0.6807064819136.5%
 
-0.4632392219136.5%
 
0.3493219822136.5%
 
0.9084721424136.5%
 
-0.4278996601136.5%
 
0.5070956074136.5%
 
-0.08510969522126.0%
 
-0.4883745934126.0%
 
-0.3518109233126.0%
 
Other values (12)5728.5%
 
ValueCountFrequency (%) 
-0.7695906706115.5%
 
-0.722721324410.5%
 
-0.6807064819136.5%
 
-0.6220739531126.0%
 
-0.596680401742.0%
 
ValueCountFrequency (%) 
3.9826963652713.5%
 
0.9084721424136.5%
 
0.6904777086115.5%
 
0.690477708621.0%
 
0.5725543597115.5%
 

NCFP_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct191
Distinct (%)96.5%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.1980761927
Minimum-1.652795634
Maximum1.604264254
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:03.714709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.652795634
5-th percentile-1.372736406
Q1-0.6872341195
median-0.327062638
Q30.2217732269
95-th percentile1.251707994
Maximum1.604264254
Range3.257059888
Interquartile range (IQR)0.9090073464

Descriptive statistics

Standard deviation0.6958859429
Coefficient of variation (CV)-3.513223539
Kurtosis0.297740018
Mean-0.1980761927
Median Absolute Deviation (MAD)0.4884854841
Skewness0.4068793787
Sum-39.21908616
Variance0.4842572455
MonotocityNot monotonic
2020-12-18T22:47:03.872567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5091133621.0%
 
-0.689914995621.0%
 
1.60426425421.0%
 
-0.500250789921.0%
 
-0.381735122521.0%
 
-0.524993260421.0%
 
0.0941799138821.0%
 
-1.561647410.5%
 
-1.06203357810.5%
 
-0.501678010110.5%
 
Other values (181)18190.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.65279563410.5%
 
-1.57395561210.5%
 
-1.561647410.5%
 
-1.55944502210.5%
 
-1.54220535110.5%
 
ValueCountFrequency (%) 
1.60426425421.0%
 
1.53721428410.5%
 
1.51308366310.5%
 
1.5091133621.0%
 
1.49728907410.5%
 

TO_100d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.4126536343
Minimum-0.9557015091
Maximum2.497548154
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:04.039545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9557015091
5-th percentile-0.9097148365
Q1-0.6241039141
median0.2624818494
Q31.220540182
95-th percentile2.14805531
Maximum2.497548154
Range3.453249663
Interquartile range (IQR)1.844644097

Descriptive statistics

Standard deviation1.037002778
Coefficient of variation (CV)2.513010166
Kurtosis-1.121444716
Mean0.4126536343
Median Absolute Deviation (MAD)0.8898857308
Skewness0.3917153171
Sum81.70541958
Variance1.075374761
MonotocityNot monotonic
2020-12-18T22:47:04.199927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.86150696521.0%
 
2.09112783621.0%
 
-0.736337329210.5%
 
0.229771783710.5%
 
0.225995115910.5%
 
-0.00569154704310.5%
 
-0.201949264610.5%
 
-0.867701668210.5%
 
-0.620264346410.5%
 
1.61575857110.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.955701509110.5%
 
-0.952555981110.5%
 
-0.95237397310.5%
 
-0.944900399610.5%
 
-0.941857652610.5%
 
ValueCountFrequency (%) 
2.49754815410.5%
 
2.49731047310.5%
 
2.48886208610.5%
 
2.36985395510.5%
 
2.36951188710.5%
 

BP_LF_min
Real number (ℝ)

MISSING

Distinct192
Distinct (%)97.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.275570381
Minimum-1.784026809
Maximum1.24609317
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:47:04.357929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.784026809
5-th percentile-1.382361948
Q1-0.8025299879
median-0.3624542368
Q30.2675511385
95-th percentile0.9949649674
Maximum1.24609317
Range3.030119979
Interquartile range (IQR)1.070081126

Descriptive statistics

Standard deviation0.7187607458
Coefficient of variation (CV)-2.608265602
Kurtosis-0.5914787174
Mean-0.275570381
Median Absolute Deviation (MAD)0.4892019542
Skewness0.1435327406
Sum-54.56293545
Variance0.5166170097
MonotocityNot monotonic
2020-12-18T22:47:04.511153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.609177621.0%
 
-0.889517463721.0%
 
0.99664224621.0%
 
1.17321960421.0%
 
-0.481524589721.0%
 
-1.61914983421.0%
 
-0.277457434310.5%
 
-0.757870398610.5%
 
-1.13097201810.5%
 
0.347228162110.5%
 
Other values (182)18291.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.78402680910.5%
 
-1.77801604910.5%
 
-1.75337984110.5%
 
-1.74917894710.5%
 
-1.61914983421.0%
 
ValueCountFrequency (%) 
1.2460931710.5%
 
1.21271548410.5%
 
1.19622145310.5%
 
1.17321960421.0%
 
1.06875059810.5%
 

Interactions

2020-12-18T22:46:31.230900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.340623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.458419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.577212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.689443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.792248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:31.905748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.019862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.129714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.235590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.336652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.448452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.549359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.653769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.760033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.859171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:32.977149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.098043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.213572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.329015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.446780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.559771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.670842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.778566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:33.890580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.000825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.116622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.226248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.341566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.457847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.573165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.682344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.803508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:34.926021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.041668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.159347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.273441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.395256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.514764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.628240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.741743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.863150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:35.980259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.102418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.220324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.334614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.454390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.567650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.682567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.793636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:36.910600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.026789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.144411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.255640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.383520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.495477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.607054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.714940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.826797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:37.947548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:38.063840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:39.670496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:39.782932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:39.902881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.017937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.134268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.244612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.355838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.467472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.578327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.683180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.791179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:40.904070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.014460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.131685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.238512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.349270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.465727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.582472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.696325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.810351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:41.930221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.039993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.153613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.266096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.369972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.483583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.593918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.703351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.818613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:42.932382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.046129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.170772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.289255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.412525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.526127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.643241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.764558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:43.886093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.002600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.114133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.234517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.351961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.470500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.583614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.697210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.806533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:44.920751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.036908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.153954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.266623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.377308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.493952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.607241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.718497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.829400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:45.947741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.055237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.167652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.284264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.393269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.503309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.612681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.731254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.843824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:46.960544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.074476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.197185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.308588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.420433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.530709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.649025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.760354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.872487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:47.987828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.095766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.210437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.320785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.442631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.555553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.665664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.775575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.885529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:48.994892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.109073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.220715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.334741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.448753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.557672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.670470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.775131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:49.889403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.016983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.152223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.273540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.388276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.508740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.623873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.741827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.855627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:50.974523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.094057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.217352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.335170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.461621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.579186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.693529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.804557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:51.924140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.054621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.171487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.281966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.398026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.512544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.626138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.734930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.846479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:52.965300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.089349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.212552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.322552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.432572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.554851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.676068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.791562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:53.904389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.024140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.141572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.259333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.371437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.485896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.601141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.716570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.827661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:54.942772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.058796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.182826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.301476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.427484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.550139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.666984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.787595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:55.906459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.025714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.141789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.258567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.377946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.499449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.618975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.739304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.853181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:56.963682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.083090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.209661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.325543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.440467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.559812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.673922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.787928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:57.899838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.016731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.133583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.249550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.361822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.476802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-18T22:47:04.667794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-18T22:47:04.880813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-18T22:47:05.074003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-18T22:47:05.281193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-18T22:46:58.696362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:58.929121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:59.196714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:46:59.454621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

dateturnover_vol_20d_maxVSTD_6d_mincurrent_ratioTO_20d_minturnover_vol_100d_minassets_turnVSTD_6d_maxPPReversal_20_minn_cashflow_actturnover_vol_5d_minDAVOL5_minopincome_of_ebtNCFP_minTO_100d_minBP_LF_min
02013-10-06-0.3545291.196680-0.441261-0.176188-0.291996-0.1573580.0510360.6897460.8589192.2169290.344705-0.0643931.2278660.1682890.454233
12013-10-13-0.322918-0.160803-0.476521-0.180896-0.3225270.2428310.0205060.6432201.0986010.0949270.136002-0.3518111.2235160.1787770.466642
22013-10-20-0.288417-0.368445-0.476521-0.163815-0.3461840.242831-0.4894140.5057211.098601-0.211482-0.406105-0.3518111.2446640.2070230.505888
32013-10-27-0.283136-0.520124-0.476521-0.197837-0.4980510.242831-0.6633270.3964811.098601-0.419145-0.537721-0.3518111.2916220.0568660.593030
42013-11-03-0.248043-0.621366-0.476521-0.395899-0.4902620.242831-0.6685240.1308221.098601-0.528168-0.845137-0.3518111.497289-0.0056920.974699
52013-11-10-0.515703-0.764025-0.476521-0.614490-0.4784860.242831-0.714226-0.2462871.098601-0.762320-0.971230-0.3518111.513084-0.0873551.004010
62013-11-17-0.743216-0.687390-0.476521-0.727739-0.4598070.242831-0.753946-0.6007591.098601-0.623085-1.001059-0.3518111.604264-0.1448221.173220
72013-11-24-0.796577-0.692775-0.476521-0.794200-0.4486800.242831-0.764006-0.7481091.098601-0.632697-0.907514-0.3518111.493376-0.2081530.967438
82013-12-01-0.853669-0.723013-0.476521-0.799913-0.4667550.242831-0.762483-0.7483651.098601-0.708266-0.768725-0.3518111.509113-0.2837640.996642
92013-12-08-0.857891-0.686717-0.476521-0.807012-0.4777480.242831-0.729887-0.6242111.098601-0.612236-0.852548-0.3518111.537214-0.3604151.048791

Last rows

dateturnover_vol_20d_maxVSTD_6d_mincurrent_ratioTO_20d_minturnover_vol_100d_minassets_turnVSTD_6d_maxPPReversal_20_minn_cashflow_actturnover_vol_5d_minDAVOL5_minopincome_of_ebtNCFP_minTO_100d_minBP_LF_min
1902017-05-28-0.759860-0.783988-0.692588-0.813412-0.828306-0.696955-0.396413-1.0846940.556155-0.889069-0.585858-0.6807060.313519-0.9214300.035999
1912017-06-04-0.736527-0.052180-0.692588-0.751755-0.820862-0.696955-0.360318-0.8674060.556155-0.1294290.223291-0.6807060.296261-0.908803-0.018361
1922017-06-11-0.734977-0.248809-0.692588-0.783900-0.823218-0.696955-0.520301-0.7662920.556155-0.3401460.035970-0.6807060.281792-0.914880-0.063935
1932017-06-18-0.733937-0.790437-0.692588-0.795377-0.843302-0.696955-0.544729-0.5310590.556155-0.894461-0.466629-0.6807060.295132-0.952374-0.021916
1942017-06-25-0.6467920.303821-0.692588-0.748365-0.832684-0.696955-0.212443-0.2929600.5561550.1864660.093221-0.6807060.263521-0.944900-0.121489
1952017-07-02-0.700936-0.683952-0.692588-0.793047-0.833489-0.696955-0.6901060.0061380.556155-0.786609-0.363788-0.6807060.269888-0.941140-0.101433
1962017-07-09-0.704476-0.386691-0.756547-0.789245-0.834881-0.422746-0.5518410.1874411.041811-0.500418-0.332105-0.5966800.640778-0.936753-0.074485
1972017-07-16-0.715600-0.652882-0.756547-0.784917-0.833255-0.422746-0.6089100.3350511.041811-0.754597-0.178711-0.5966800.649679-0.941858-0.054267
1982017-07-23-0.836321-0.379001-0.756547-0.830048-0.833601-0.422746-0.6397010.2960551.041811-0.442337-0.096974-0.5966800.718870-0.9525560.102905
1992017-07-30-0.847037-0.700613-0.756547-0.842643-0.832192-0.422746-0.7740330.1799871.041811-0.804742-0.324637-0.5966800.699284-0.9557020.058415